Does not crop the classified las file

las_manipulation.py

@@ -406,8 +406,8 @@ def process(yaml_file):
     # Interpolate point cloud onto a grid
     print('Interpolating to grid...')
     xyz_name = os.path.join(tmp_dir, las_basename + '.xyz')
-    call_lastools('las2dem', input=las_classified_name, output=xyz_name,
+    call_lastools('blast2dem', input=las_classified_name, output=xyz_name,
-                  args=['-step', 1], verbose=False)
+                  args=['-step', 0.1], verbose=False)
 
     # Make runup clipping mask from gridded point cloud
     print('Calculating runup clipping mask...')

las_outputs.py

@@ -87,14 +87,14 @@ def plot_profiles(profile_name, csv_output_dir, graph_loc, ch_limits, delta_vol,
         fig_h = profiles.dropna().values.max() / m_per_inch * vertical_exag
         fig_w = (profiles.index.max() - ch_min) / m_per_inch
     except ValueError:
-        fig_h = 5
+        fig_h = 2.3
         fig_w = 10
 
     if scale_figures:
         fig, ax = plt.subplots(figsize=(fig_w, fig_h))
         ax.set_aspect(vertical_exag)
     else:
-        fig, ax = plt.subplots(figsize=(10, 5))
+        fig, ax = plt.subplots(figsize=(10, 2.3))
 
 
     for col in profiles.columns:
@@ -190,14 +190,15 @@ def calculate_volumes(profile_name, current_survey_date, previous_survey_date,
     volumes = volumes.sort_index()
     volumes.to_csv(csv_vol)
 
-    # Get most recent volume difference for current profile
-    current_date_idx = volumes.columns.get_loc('Volume_' + current_survey_date)
-    previous_date_idx = volumes.columns.get_loc('Volume_' + previous_survey_date)
 
-    if previous_date_idx < 0:
+
+    if previous_survey_date=="nan":
         # Return None if there is only one survey
         delta_vol = None
     else:
+        # Get most recent volume difference for current profile
+        current_date_idx = volumes.columns.get_loc('Volume_' + current_survey_date)
+        previous_date_idx = volumes.columns.get_loc('Volume_' + previous_survey_date)
         previous_vol = volumes.loc[profile_name][previous_date_idx]
         current_vol = volumes.loc[profile_name][current_date_idx]
         delta_vol = current_vol - previous_vol
@@ -239,32 +240,11 @@ def process(yaml_file):
     # Get name of swash cropping polygon
     crop_swash_poly = os.path.join(shp_swash_dir, las_basename + '.shp')
 
-    # Crop point cloud to swash boundary
-    print('Cropping swash...')
-    las_data = call_lastools('lasclip', input=input_las, output='-stdout',
-                             args=['-poly', crop_swash_poly], verbose=False)
-
-    # Export classified, clipped las for delivery to client
-    las_name = os.path.join(output_las_dir, las_basename + '.las')
-    with open (las_name, 'wb') as f:
-        f.write(las_data)
-
-    # Apply sea-side clipping polygon
-    print('Cropping back of beach...')
-    las_data = call_lastools('lasclip', input=las_data, output='-stdout',
-                             args=['-poly', crop_heatmap_poly], verbose=False)
-
-
-    # Create clipping polygon for heatmap raster
-    print('Creating heat map cropping polygon...')
-    shp_name = os.path.join(output_poly_dir, las_basename + '.shp')
-    call_lastools('lasboundary', input=las_data, output=shp_name, verbose=False)
-
     # Make a raster from point cloud
     print('Creating heat map raster...')
     tif_name = os.path.join(output_tif_dir, las_basename + '.tif')
-    call_lastools('las2dem', input=las_data, output=tif_name,
+    call_lastools('blast2dem', input=input_las, output=tif_name,
-                  args=['-step', 1, '-keep_class', 2], verbose=False)
+                  args=['-step', 0.1, '-keep_class', 2], verbose=True)
     # IF THIS STEP ISN'T WORKING:
         # might mean there are no data lines
         # trying running with args=['-step', 1, '-keep_class', 2, '-rescale', 0.001,0.001,0.001]
@@ -274,7 +254,7 @@ def process(yaml_file):
     # Extract elevations along profiles from triangulated surface
     print('Extracting profile elevations...')
     df = extract_pts(
-        las_data,
+        input_las,
         cp_csv,
         survey_date,
         beach,